Google Cloud Platform (GCP) was the first and only major cloud vendor to offer NVIDIA’s newest data center GPU, the Tesla T4, via a private alpha. Today, these T4 GPU instances are now available publicly in beta in Brazil, India, Netherlands, Singapore, Tokyo, and the United States. For Brazil, India, Japan, and Singapore, these are the first GPUs we have offered in those GCP regions.

Fast, cost-effective ML inference

The T4 GPU is well suited for many machine learning, visualization and other GPU accelerated workloads. Each T4 comes with 16GB of GPU memory, offers the widest precision support (FP32, FP16, INT8 and INT4), includes NVIDIA Tensor Core and RTX real-time visualization technology and performs up to 260 TOPS1 of compute performance. Customers can create custom VM shapes that best meet their needs with up to four T4 GPUs, 96 vCPUs, 624GB of host memory and optionally up to 3TB of in-server local SSD.

Machine learning inference

The T4 is the best GPU in our product portfolio for running inference workloads. Its high performance characteristics for FP16, INT8 and INT4 allow you to run high scale inference with flexible accuracy/performance tradeoffs that are not available on any other GPU. The T4’s 16GB of memory supports large ML models or running inference on multiple smaller models simultaneously. ML inference performance on Google Compute Engine’s T4s has been measured at up to 4267 images/sec2 with latency as low as 1.1ms3. Running production workloads on T4 GPUs on Compute Engine is a great solution thanks to the T4’s price, performance, global availability across eight regions and high-speed Google network.

Strong visualization with RTX

The NVIDIA T4 with its Turing architecture is the first data center GPU to include dedicated ray-tracing processors. Called RT Cores, they accelerate the computation of how light travels in 3D environments. Turing accelerates real-time ray tracing over the previous-generation NVIDIA Pascal architecture and can render final frames for film effects faster than CPUs, providing hardware-accelerated ray tracing capabilities via NVIDIA’s OptiX ray-tracing API. In addition, we are glad to also offer virtual workstations running on T4 GPUs that give creative and technical professionals the power of the next generation of computer graphics with the flexibility to work from anywhere and on any device.

Getting started

Google makes it easy to get started with T4 GPUs for ML, compute and visualization. Check out GPU product page to learn more about the T4 and other GPU offerings. For those looking to get up and running quickly with GPUs and Compute Engine, Deep Learning VM image comes with NVIDIA drivers and various ML libraries pre-installed.